The invention relates generally to the field of digital computer-assisted information and knowledge processing systems, and more particularly to a system and method for adaptively managing changing behavior of a complex entity using knowledge acquired through artificial intelligence inductive learning techniques.
Artificial intelligence ("AI") research, which is a part of the field of computer science, is directed to the development of systems, using digital computers, to mimic the performance of human intelligent behavior. Since the late 1970's, AI research has gradually evolved from developing small prototypes in academic research laboratories to development of robust systems that can make significant impacts on many real-world tasks outside the laboratory. Of particular importance among AI-related applications are knowledge-based expert systems which incorporate knowledge from human experts, in particular application domains, to provide advice to users.
While different application domains often require a number of different detailed considerations, development of knowledge based expert systems generally involves three general considerations in common, namely, knowledge acquisition, knowledge representation and knowledge utilization. Of the three general considerations, the most important is knowledge acquisition, since most current knowledge-based expert systems use deductive inference techniques. Currently, development of knowledge-based expert systems requires a knowledge engineer to interview, in many cases iteratively in a number of interview sessions, one or more human experts. Problems arise since the interviews are often ill-structured, time consuming and prone to error. Furthermore, the assistance that deductive knowledge-based expert systems can provide is generally limited by the quantity, variety and accuracy of the knowledge that it contains.
Furthermore, the knowledge acquired in this manner is generally heuristic, that is, it represents facts and empirical associations, or patterns, that experts in the particular domain have developed over long periods of time in working with particular examples in the domain. Heuristic knowledge is often of limited utility, since it is not easily verified and it is difficult to update. Furthermore, deductive knowledge-based expert systems typically can only be used in connection with circumstances within the domain of the knowledge which they contain, and, if a circumstance is unexpected and outside of that domain, the expert system will not be able to provide assistance in that circumstance.
AI researchers are also working in areas of machine learning techniques, to permit computers to acquire knowledge directly, rather than or in addition to, requiring knowledge to be provided by experts. Several knowledge acquisition techniques use inductive inference to infer knowledge in particular domains from training examples in those domains. These inductive learning systems generate, from the information provided as domain examples, domain knowledge that effectively comprises descriptions of generic patterns. If properly designed, the transition from information to knowledge can serve as a potential remedy to the knowledge acquisition problem. In the past, researchers have suggestion that inductive learning techniques be applied as automatic knowledge acquisition tools in developing knowledge-based expert systems, using training examples collected from data from actual operation in the domain or from simulation models.